We also experiment with convolutional neural networks (CNNs) as RCA classifiers. Here, we utilize DeepMedicCode available at https://github.com/Kamnitsask/deepmedic, a 3D CNN architecture for automatic segmentation [1]}. The architecture is computationally efficient as it can handle large image context by using a dual pathway for multi-scale processing. CNNs have been shown to be able to learn highly complex and discriminative data associations between input data and target output. The architecture of the network is defined by the number of layers and the number of activation functions in each layer. In CNNs, each activation function corresponds to a learned convolutional filter, and each filter produces a feature map (FM) by convolving the outputs of the previous layer. Through the sequential application of many convolutions, highly complex features are learned that are then used to produce voxel-wise predictions at the final, fully-connected layer. CNNs are a type of deep learning approach which normally requires large amounts of training data in order to perform well due to the thousands (or millions) of parameters corresponding to the weights of the filters. To be able to act as a RCA classifier that is trained on a single image, we require a specialised architecture. Here, we reduce the number of FMs in each layer by one third compared to the default setting of DeepMedic. We also cut the feature maps in the last fully connected layers, from 150 to 45. By reducing the feature maps without changing the architecture in terms of number of layers the network preserves its capability to see large image context as the size of the receptive field remains unchanged. With less number of filters, the number of parameters is substantially decreased, which leads to faster computations, but more importantly, reduces overfitting when trained on a single image. Training is performed in a patch-wise manner where the original input image is devided into 3D patches that are then sampled during training using backpropagation and batch normalization. For details about the training procedure and further analysis of DeepMedic we refer to [1]}.
